code: def build_model_filename(args):
      embedding = utils.embedding_name(args)
      maybe_finetune = (
          "finetuned"
          if args["bert_finetune"] or args["stage1_bert_finetune"]
          else ""
      )
      transformer_finetune_begin = (
          "%d" % args["bert_finetune_begin_epoch"]
          if args["bert_finetune_begin_epoch"] is not None
          else ""
      )
      model_save_file = args["save_name"].format(
          shorthand=args["shorthand"],
          embedding=embedding,
          finetune=maybe_finetune,
          transformer_finetune_begin=transformer_finetune_begin,
          transition_scheme=args["transition_scheme"]
          .name.lower()
          .replace("_", ""),
          trans_layers=args["bert_hidden_layers"],
          seed=args["seed"],
      )
      model_save_file = re.sub("_+", "_", model_save_file)
      logger.info("Expanded save_name: %s", model_save_file)
  
      model_dir = os.path.split(model_save_file)[0]
      if model_dir != args["save_dir"]:
          model_save_file = os.path.join(
              args["save_dir"], model_save_file
          )
      return model_save_file
  
file_path: stanza/stanza/models/constituency_parser.py
imports: import argparse
  import logging
  import os
  import re
  import torch
  import stanza
  from stanza.models.common import constant
  from stanza.models.common import utils
  from stanza.models.constituency import retagging
  from stanza.models.constituency import trainer
  from stanza.models.constituency.lstm_model import ConstituencyComposition, SentenceBoundary, StackHistory
  from stanza.models.constituency.parse_transitions import TransitionScheme
  from stanza.models.constituency.utils import DEFAULT_LEARNING_EPS, DEFAULT_LEARNING_RATES, DEFAULT_MOMENTUM, DEFAULT_LEARNING_RHO, DEFAULT_WEIGHT_DECAY, NONLINEARITY, add_predict_output_args, postprocess_predict_output_args
  from stanza.resources.common import DEFAULT_MODEL_DIR
label_desc: Use image node_id 2 for calling a global function with example usage: build_model_filename(args) and returns: model_save_file